AAAAAA
Results:
1-20
|
Results: 20
Authors:
Scholkopf, B
Burges, CJC
Smola, AJ
Citation: B. Scholkopf et al., Advances in kernel methods - Support vector learning - Introduction, ADVANCES IN KERNEL METHODS, 1999, pp. 1-15
Citation: Roadmap, ADVANCES IN KERNEL METHODS, 1999, pp. 17-22
Authors:
Vapnik, V
Citation: V. Vapnik, Three remarks on the support vector method of function estimation, ADVANCES IN KERNEL METHODS, 1999, pp. 25-41
Authors:
Bartlett, P
Shawe-Taylor, J
Citation: P. Bartlett et J. Shawe-taylor, Generalization performance of support vector machines and other pattern classifiers, ADVANCES IN KERNEL METHODS, 1999, pp. 43-54
Authors:
Cristianini, N
Shawe-Taylor, J
Citation: N. Cristianini et J. Shawe-taylor, Bayesian voting schemes and large margin classifiers, ADVANCES IN KERNEL METHODS, 1999, pp. 55-67
Authors:
Wahba, G
Citation: G. Wahba, Support vector machines, reproducing kernel Hilbert spaces, and randomizedGACV, ADVANCES IN KERNEL METHODS, 1999, pp. 69-88
Authors:
Burges, CJC
Citation: Cjc. Burges, Geometry and invariance in Kernel based methods, ADVANCES IN KERNEL METHODS, 1999, pp. 89-116
Authors:
Opper, M
Citation: M. Opper, On the annealed VC entropy for margin classifiers: A statistical mechanicsstudy, ADVANCES IN KERNEL METHODS, 1999, pp. 117-126
Authors:
Williamson, RC
Smola, AJ
Scholkopf, B
Citation: Rc. Williamson et al., Entropy numbers, operators and support vector kernels, ADVANCES IN KERNEL METHODS, 1999, pp. 127-144
Authors:
Kaufman, L
Citation: L. Kaufman, Solving the quadratic programming problem arising in support vector classification, ADVANCES IN KERNEL METHODS, 1999, pp. 147-167
Authors:
Joachims, T
Citation: T. Joachims, Making large-scale support vector machine learning practical, ADVANCES IN KERNEL METHODS, 1999, pp. 169-184
Authors:
Platt, JC
Citation: Jc. Platt, Fast training of support vector machines using sequential minimal optimization, ADVANCES IN KERNEL METHODS, 1999, pp. 185-208
Authors:
Mattera, D
Haykin, S
Citation: D. Mattera et S. Haykin, Support vector machines for dynamic reconstruction of a chaotic system, ADVANCES IN KERNEL METHODS, 1999, pp. 211-241
Authors:
Muller, KR
Smola, AJ
Ratsch, G
Scholkopf, B
Kohlmorgen, J
Vapnik, V
Citation: Kr. Muller et al., Using support vector machines for time series prediction, ADVANCES IN KERNEL METHODS, 1999, pp. 243-253
Authors:
Kressel, UHG
Citation: Uhg. Kressel, Pairwise classification and support vector machines, ADVANCES IN KERNEL METHODS, 1999, pp. 255-268
Authors:
Osuna, EE
Girosi, F
Citation: Ee. Osuna et F. Girosi, Reducing the run-time complexity in support vector machines, ADVANCES IN KERNEL METHODS, 1999, pp. 271-283
Authors:
Stitson, MO
Gammerman, A
Vapnik, V
Vovk, V
Watkins, C
Weston, J
Citation: Mo. Stitson et al., Support vector regression with ANOVA decomposition kernels, ADVANCES IN KERNEL METHODS, 1999, pp. 285-291
Authors:
Weston, J
Gammerman, A
Stitson, MO
Vapnik, V
Vovk, V
Watkins, C
Citation: J. Weston et al., Support vector density estimation, ADVANCES IN KERNEL METHODS, 1999, pp. 293-305
Authors:
Bennett, KP
Citation: Kp. Bennett, Combining support vector and mathematical programming methods for classification, ADVANCES IN KERNEL METHODS, 1999, pp. 307-326
Authors:
Scholkopf, B
Smola, AJ
Muller, KR
Citation: B. Scholkopf et al., Kernel principle component analysis, ADVANCES IN KERNEL METHODS, 1999, pp. 327-352
Risultati:
1-20
|